US covid_19 data from NYT
Lisa Lendway
Load libraries and set theme.
library(tidyverse)
library(lubridate)
library(gganimate)
library(knitr)
library(geofacet)
theme_set(theme_minimal())The data come from the New York Times covid-19 data github repo.
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")Create a variable for days since the number of cases was over 20. Also create some summarized data that will be used later.
covid19_comp_date <- covid19 %>%
group_by(state) %>%
arrange(date) %>%
mutate(cases_over20 = cases > 20) %>%
filter(sum(cases_over20) > 0) %>%
mutate(min_cases_over20 = which.max(cases_over20),
days_since_over20 = row_number() - min_cases_over20) %>%
ungroup() %>%
mutate(state_ordered = fct_reorder2(state,
date,
cases))
#covid19_comp_date
max_days <- max(covid19_comp_date$days_since_over20)
for_labels <- covid19_comp_date %>%
group_by(state_ordered) %>%
summarize(max_case = max(cases),
max_days_since20 = max(days_since_over20))#test plots
covid19_comp_date %>%
ggplot(aes(x = days_since_over20, y = cases, group = state_ordered)) +
geom_line() +
coord_cartesian(xlim = c(-10, max_days)) +
geom_text(aes(x = max_days_since20,
y = max_case,
label = state_ordered),
color = "gray",
data = for_labels) +
scale_y_log10() +
# facet_geo(~ state_ordered) #returns an error
facet_wrap(~ state_ordered)Create a gganimate plot that shows the number of cases over time for each state.
covid19_anim <- covid19_comp_date %>%
ggplot(aes(x = days_since_over20, y = cases,
group = state_ordered)) +
geom_line() +
coord_cartesian(xlim = c(-10, max_days)) +
geom_text(aes(x = max_days_since20,
y = max_case,
label = state_ordered),
data = for_labels) +
scale_y_log10(breaks = scales::trans_breaks("log10",
function(x) 10^x),
labels = scales::comma) +
annotation_logticks(sides = "l") +
labs(#title = 'State/Territory: {closest_state}',
x = "Days since # of cases greater than 20",
y = "Cumulative Cases",
caption = "data source: https://github.com/nytimes/covid-19-data") +
transition_states(states = state_ordered,
transition_length = 1,
state_length = 5) +
shadow_mark(color = "gray", future = TRUE)Create the animation (this takes a bit of time to run). Use code chunk option eval=FALSE so this isn’t evaluated when the file is knit.
animate(covid19_anim, nframes = 100, duration = 30)Save the animation.
anim_save("covid19_us.gif")Reload the animation so we can see it here.
knitr::include_graphics("covid19_us.gif")
Add an animation that shows counts by state over time. First, do some cleaning of the data and add static plot.
covid_data_for_barplot <- covid19_comp_date %>%
ungroup() %>%
filter(date >= mdy("03/05/2020")) %>%
group_by(date) %>%
mutate(rank = row_number(desc(cases))) %>%
arrange(date, rank) %>%
ungroup() %>%
filter(rank <= 10)
static_plot <- covid_data_for_barplot %>%
ggplot(aes(x = rank, group = state,
fill = as.factor(state),
color = as.factor(state))) +
geom_tile(aes(y = cases/2,
height = cases,
width = 0.9), alpha = 0.8, color = NA) +
geom_text(aes(y = 0, label = paste(state, " ")),
vjust = 0.2, hjust = 1) +
geom_text(aes(y = cases, label = as.character(cases), hjust=0)) +
coord_flip(clip = "off", expand = FALSE) +
scale_y_continuous(labels = scales::comma) +
scale_x_reverse() +
scale_color_viridis_d() +
scale_fill_viridis_d() +
guides(color = FALSE, fill = FALSE)+
theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.grid.major.x = element_line( size=.1, color="grey" ),
panel.grid.minor.x = element_line( size=.1, color="grey" ),
plot.title = element_text(face = 'bold', size = 16),
plot.subtitle = element_text(size = 14),
plot.caption =element_text(size=10, face="italic", color="grey"),
plot.background=element_blank(),
plot.margin = margin(2,2, 2, 4, "cm"))Animation
covid_bar_anim <-
static_plot +
transition_states(date,
transition_length = 4,
state_length = 3) +
ease_aes('cubic-in-out') +
view_follow(fixed_x = TRUE) +
labs(title = 'Date : {closest_state}',
subtitle = "Count of Covid-19 cases, top 10 states ",
caption = "data source: https://github.com/nytimes/covid-19-data")
animate(covid_bar_anim, nframes = 200, duration = 30)Save the animation.
anim_save("covid_us_barplot.gif")Reload the animation so we can see it here.
knitr::include_graphics("covid_us_barplot.gif")
Trajectory graph
Inspired by Aatish Bhatia’s graph.
trajectory_data <- covid19_comp_date %>%
group_by(state_ordered) %>%
mutate(cases_lag_3day = lag(cases, 3, order_by = date)) %>%
# select(date, state_ordered, cases, cases_lag_3day) %>%
ungroup() %>%
filter(days_since_over20 >= 0) %>%
replace_na(list(cases_lag_3day = 0)) %>%
mutate(new_cases_3day = cases - cases_lag_3day) %>%
arrange(state_ordered, date)
#trajectory_dataBuild the static plot
trajectory_animated <- trajectory_data %>%
ggplot(aes(x = cases, y = new_cases_3day,
group = state_ordered)) +
geom_path(color = "gray") +
geom_point(color = "turquoise", size = .5) +
geom_text(aes(label = state_ordered), check_overlap = TRUE) +
scale_y_log10(breaks = scales::trans_breaks("log10",
function(x) 10^x),
labels = scales::comma) +
scale_x_log10(breaks = scales::trans_breaks("log10",
function(x) 10^x),
labels = scales::comma) +
labs(x = "Total Cases",
y = "New cases (in past 3 days)",
title = 'Date: {frame_along}',
caption = "data source: https://github.com/nytimes/covid-19-data, \n inspired by: https://aatishb.com/covidtrends/") +
transition_reveal(date)Animate
animate(trajectory_animated, nframes = 200, duration = 30)Save the animation
anim_save("covid_trajectory.gif")Reload the animation so we can see it here.
knitr::include_graphics("covid_trajectory.gif")